Improving Library Characterization Quality And Runtime With Machine Learning


By Megan Marsh and Wei-Lii Tan Today’s semiconductor applications, ranging from advanced sensory applications, IoT, edge computing devices, high performance computing, to dedicated A.I. chips, are constantly pushing the boundaries of attainable power, performance, and area (PPA) metrics. The race to design and ship these innovative devices has resulted in a focused, time-to-market-driven e... » read more

From Physics To Applications


Jack Harding, president and CEO of eSilicon, sat down with Semiconductor Engineering to talk about the shift toward AI and advanced packaging, and the growing opportunities at 7nm at a time when Moore's Law has begun slowing down. What follows are excerpts of that conversation. SE: Over the past year, the industry has changed its focus from shrinking features and consolidation to all sorts o... » read more

System Bits: Oct. 23


Adapting machine learning for use in scientific research To better tailor machine learning for effective use in scientific research, the U.S. Department of Energy has awarded a collaborative grant to a group of researchers, including UC Santa Barbara mathematician Paul Atzberger, to establish a new data science research center. According to UCSB, the Physics-Informed Learning Machines for M... » read more

Looking For The Next Big Innovation


Never has there been more demand for “The Big Innovation” — one that moves the needle for performance, power and area-cost (PPAC) in a big way — as there is in the current era of AI and machine learning (ML). As summarized in Why AI Workloads Require New Computing Architectures, AI workloads require new architectures to process data. These workloads also call for heterogeneous comp... » read more

Machine Learning Invades IC Production


Semiconductor Engineering sat down to discuss artificial intelligence (AI), machine learning, and chip and photomask manufacturing technologies with Aki Fujimura, chief executive of D2S; Jerry Chen, business and ecosystem development manager at Nvidia; Noriaki Nakayamada, senior technologist at NuFlare; and Mikael Wahlsten, director and product area manager at Mycronic. What follows are excerpt... » read more

Machine Learning Based Prediction: Health Behavior on BP


Source: UC San Diego Jacobs School of Engineering, Po-Han Chiang and Sujit Dey, Mobile Systems Design Lab, Dept. of Electrical and Computer Engineering Using wearable off-the-shelf technology and machine learning, UC San Diego researchers have developed a method to predict an individual’s blood pressure and provide personalized recommendations to lower it based on this data. The researc... » read more

Adding AI To The IoT


The Internet of Things is about to undergo a radical change, fueled by vast number of things coupled with an almost pervasive presence of AI. The IoT today encompasses a long list of vertical markets, all of them connected to the Internet but not necessarily to each other. The concept of the IoT really began taking off in 2015, when a combination of data analytics, high-speed, affordable and... » read more

Week in Review: IoT, Security, Auto


Deals Dialog Semiconductor made a blockbuster deal with Apple – the chip company will license power management technologies and transfer some assets to Apple, which will use them in their internal chip research and development. More than 300 Dialog employees, mostly engineers, will join Apple, which will pay $300 million in cash for the transaction and prepay another $300 million for Dialog ... » read more

ML Becomes Useful For Variation Coverage


According to industry sources, it is quite a feat to get a chip back from the foundry that actually meets the specifications the design team worked towards, and because of this much effort is underway across the industry to understand what will happen to a design once it reaches the manufacturing stage, and what the effects of design choices actually are. AI and ML are absolutely the buzz wo... » read more

Reliability, Machine Learning And Advanced Packaging


Semiconductor Engineering sat down to discuss reliability, resilience, machine learning and advanced packaging with Rahul Goyal, vice president in the technology and manufacturing group at Intel; Rob Aitken, R&D fellow at Arm; John Lee, vice president and general manager of the semiconductor business unit at ANSYS; and Lluis Paris, director of IP portfolio marketing at TSMC. What follows ar... » read more

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